18 January 2018 Segmenting overlapping nano-objects in atomic force microscopy image
Author Affiliations +
J. of Nanophotonics, 12(1), 016003 (2018). doi:10.1117/1.JNP.12.016003
Recently, techniques for nanoparticles have rapidly been developed for various fields, such as material science, medical, and biology. In particular, methods of image processing have widely been used to automatically analyze nanoparticles. A technique to automatically segment overlapping nanoparticles with image processing and machine learning is proposed. Here, two tasks are necessary: elimination of image noises and action of the overlapping shapes. For the first task, mean square error and the seed fill algorithm are adopted to remove noises and improve the quality of the original image. For the second task, four steps are needed to segment the overlapping nanoparticles. First, possibility split lines are obtained by connecting the high curvature pixels on the contours. Second, the candidate split lines are classified with a machine learning algorithm. Third, the overlapping regions are detected with the method of density-based spatial clustering of applications with noise (DBSCAN). Finally, the best split lines are selected with a constrained minimum value. We give some experimental examples and compare our technique with two other methods. The results can show the effectiveness of the proposed technique.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Qian Wang, Yuexing Han, Qing Li, Bing Wang, Akihiko Konagaya, "Segmenting overlapping nano-objects in atomic force microscopy image," Journal of Nanophotonics 12(1), 016003 (18 January 2018). https://doi.org/10.1117/1.JNP.12.016003 Submission: Received 20 July 2017; Accepted 15 December 2017
Submission: Received 20 July 2017; Accepted 15 December 2017

Image segmentation

Atomic force microscopy


Digital watermarking

Algorithm development

Digital imaging

Image processing algorithms and systems

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